Faculty of Engineering & Technology, Multimedia University, Melaka 75450, Malaysia.
Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia.
Int J Environ Res Public Health. 2022 Oct 21;19(20):13702. doi: 10.3390/ijerph192013702.
Rivers are the main sources of freshwater supply for the world population. However, many economic activities contribute to river water pollution. River water quality can be monitored using various parameters, such as the pH level, dissolved oxygen, total suspended solids, and the chemical properties. Analyzing the trend and pattern of these parameters enables the prediction of the water quality so that proactive measures can be made by relevant authorities to prevent water pollution and predict the effectiveness of water restoration measures. Machine learning regression algorithms can be applied for this purpose. Here, eight machine learning regression techniques, including decision tree regression, linear regression, ridge, Lasso, support vector regression, random forest regression, extra tree regression, and the artificial neural network, are applied for the purpose of water quality index prediction. Historical data from Indian rivers are adopted for this study. The data refer to six water parameters. Twelve other features are then derived from the original six parameters. The performances of the models using different algorithms and sets of features are compared. The derived water quality rating scale features are identified to contribute toward the development of better regression models, while the linear regression and ridge offer the best performance. The best mean square error achieved is 0 and the correlation coefficient is 1.
河流是世界人口的主要淡水供应源。然而，许多经济活动导致了河水污染。可以使用各种参数来监测河水水质，例如 pH 值、溶解氧、总悬浮固体和化学性质。分析这些参数的趋势和模式可以预测水质，以便相关当局采取主动措施防止水污染，并预测水恢复措施的效果。可以应用机器学习回归算法来实现这一目的。在这里，应用了八种机器学习回归技术，包括决策树回归、线性回归、岭回归、套索回归、支持向量回归、随机森林回归、极端树回归和人工神经网络，以进行水质指数预测。本研究采用了印度河流的历史数据。这些数据涉及六个水质参数。然后从原始的六个参数中推导出十二个其他特征。比较了使用不同算法和特征集的模型的性能。确定了推导的水质等级特征有助于开发更好的回归模型，而线性回归和岭回归提供了最佳性能。最佳均方误差为 0，相关系数为 1。
